store = {}
store['args']={'num_inference_samples': 10, 'available_sample_k': 5, 'seed': 482739, 'type': 'AcquisitionFunction.random', 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST_balanced with random acquisition', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'initial_percentage': 50, 'reduce_percentage': 10, 'min_remaining_percentage': 30, 'min_candidates_per_acquired_item': 100, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 'emnist_balanced_independent_random_k10_b5_482739', 'experiments_laaos': './experiment_configs/emnist_random/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=emnist_balanced_independent_random_k10_b5_482739', '--experiments_laaos=./experiment_configs/emnist_random/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.01829787234042553, 'nll': 3.8654186764232654}, 'chosen_samples': [83795, 74530, 30644, 13601, 34807], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 20.93115065799998, 'batch_acquisition_elapsed_time': 0.01927856099996461})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.04324468085106383, 'nll': 54.085504550543234}, 'chosen_samples': [21643, 63986, 105297, 86576, 96864], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.81265647300006, 'batch_acquisition_elapsed_time': 0.0028581730000496464})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.05728723404255319, 'nll': 40.85688242387011}, 'chosen_samples': [100475, 105373, 107656, 50211, 16522], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 49.17076100700001, 'batch_acquisition_elapsed_time': 0.0025187990000858917})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.10367021276595745, 'nll': 42.918138045894345}, 'chosen_samples': [77695, 55530, 38101, 18548, 71153], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.32465671299997, 'batch_acquisition_elapsed_time': 0.0025904450000098223})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.1023404255319149, 'nll': 31.86074067107667}, 'chosen_samples': [30059, 110078, 96451, 73049, 88567], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 48.565075020999984, 'batch_acquisition_elapsed_time': 0.0023920969999835506})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.11164893617021276, 'nll': 26.453372789812857}, 'chosen_samples': [91772, 71789, 1433, 43293, 35263], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.287866541000085, 'batch_acquisition_elapsed_time': 0.002574131999836027})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.14606382978723403, 'nll': 26.235275778169324}, 'chosen_samples': [50520, 62651, 37216, 9813, 70922], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 58.28028079499995, 'batch_acquisition_elapsed_time': 0.0027445549999356444})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1778723404255319, 'nll': 25.7944910116741}, 'chosen_samples': [93945, 97858, 105255, 5733, 65323], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.27549420599985, 'batch_acquisition_elapsed_time': 0.002618863999941823})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.16904255319148936, 'nll': 26.200061331424937}, 'chosen_samples': [67257, 67607, 3447, 3814, 104052], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 68.35164888600002, 'batch_acquisition_elapsed_time': 0.0027573209999900428})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.18090425531914894, 'nll': 20.923588617776304}, 'chosen_samples': [93671, 70852, 21862, 90098, 22290], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.985606882999946, 'batch_acquisition_elapsed_time': 0.002791147999914756})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1955851063829787, 'nll': 18.425672128376803}, 'chosen_samples': [33087, 23987, 39253, 87478, 29678], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.113832081000055, 'batch_acquisition_elapsed_time': 0.0026534250000622706})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.20372340425531915, 'nll': 18.894882637507735}, 'chosen_samples': [75421, 51793, 67160, 6117, 43326], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.52835812399985, 'batch_acquisition_elapsed_time': 0.002817576000097688})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22196808510638297, 'nll': 17.562465011519954}, 'chosen_samples': [40625, 17736, 57976, 53953, 53329], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.85683830500011, 'batch_acquisition_elapsed_time': 0.0023857420001149876})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2226063829787234, 'nll': 17.739776948267473}, 'chosen_samples': [38238, 5650, 97888, 33654, 107962], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.423215114000186, 'batch_acquisition_elapsed_time': 0.0024578830000336893})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23585106382978724, 'nll': 14.838851444562701}, 'chosen_samples': [49591, 63855, 55212, 36091, 52860], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.81691201600006, 'batch_acquisition_elapsed_time': 0.0026762410000173986})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23606382978723403, 'nll': 16.804672826285692}, 'chosen_samples': [24781, 1628, 93224, 41149, 43823], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.050216628000044, 'batch_acquisition_elapsed_time': 0.002827454999987822})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2402659574468085, 'nll': 14.920226463321045}, 'chosen_samples': [61531, 21700, 42418, 42393, 86779], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.17621689799989, 'batch_acquisition_elapsed_time': 0.0026235769998947944})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2548936170212766, 'nll': 15.579445324717053}, 'chosen_samples': [85074, 9731, 57714, 71169, 111825], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.28397415099994, 'batch_acquisition_elapsed_time': 0.0025233750000097643})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2534574468085106, 'nll': 15.205800383870907}, 'chosen_samples': [111247, 3918, 108051, 70579, 455], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.038977596999985, 'batch_acquisition_elapsed_time': 0.002715161999958582})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2534574468085106, 'nll': 13.80346047867739}, 'chosen_samples': [95211, 40933, 101102, 71743, 104215], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.794223012999964, 'batch_acquisition_elapsed_time': 0.002778402000103597})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2799468085106383, 'nll': 13.618971259369177}, 'chosen_samples': [101239, 107927, 30242, 87023, 63], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.05224585099995, 'batch_acquisition_elapsed_time': 0.002662325999835957})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28638297872340424, 'nll': 13.325798123533426}, 'chosen_samples': [79603, 22591, 57642, 15264, 46866], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.06117630300014, 'batch_acquisition_elapsed_time': 0.002545302000044103})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28856382978723405, 'nll': 13.04892767512608}, 'chosen_samples': [22960, 75725, 29722, 3529, 7633], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.058574018999934, 'batch_acquisition_elapsed_time': 0.0023146799999267387})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2970212765957447, 'nll': 11.311222443508656}, 'chosen_samples': [97028, 59782, 30964, 67099, 35300], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.20505763700021, 'batch_acquisition_elapsed_time': 0.0026581780000469735})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3153191489361702, 'nll': 11.016229748251593}, 'chosen_samples': [25376, 37730, 20813, 3113, 36353], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.87488334599993, 'batch_acquisition_elapsed_time': 0.002299417999893194})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.33643617021276595, 'nll': 10.364523600124297}, 'chosen_samples': [90260, 55183, 44169, 82531, 93935], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.37507669999991, 'batch_acquisition_elapsed_time': 0.002537330999984988})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.32920212765957446, 'nll': 10.899063592877159}, 'chosen_samples': [63311, 62759, 27543, 90343, 56023], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.82557106499985, 'batch_acquisition_elapsed_time': 0.0028467239999372396})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3354255319148936, 'nll': 9.521097867564636}, 'chosen_samples': [30421, 58997, 16198, 16906, 992], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.11533257399992, 'batch_acquisition_elapsed_time': 0.002917335000120147})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35095744680851065, 'nll': 9.468068379609825}, 'chosen_samples': [50539, 10496, 72180, 91765, 44331], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.854947412, 'batch_acquisition_elapsed_time': 0.002675853000027928})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3468085106382979, 'nll': 9.180926591664551}, 'chosen_samples': [93501, 55400, 8602, 94872, 31457], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.40410487000008, 'batch_acquisition_elapsed_time': 0.002495874999794978})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3659574468085106, 'nll': 8.707027852265758}, 'chosen_samples': [11127, 109628, 66931, 52189, 34698], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.467164009999806, 'batch_acquisition_elapsed_time': 0.002560323999659886})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3759574468085106, 'nll': 7.826346839948223}, 'chosen_samples': [90134, 23814, 23426, 47924, 53783], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.23968692900007, 'batch_acquisition_elapsed_time': 0.0027126660002068093})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38085106382978723, 'nll': 8.0066498090777}, 'chosen_samples': [10519, 14337, 561, 60466, 97399], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.80939011400005, 'batch_acquisition_elapsed_time': 0.0024992720000227564})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.35877659574468085, 'nll': 8.59272249144633}, 'chosen_samples': [53424, 61065, 104651, 63322, 38508], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.86607646799985, 'batch_acquisition_elapsed_time': 0.0025649649996921653})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38611702127659575, 'nll': 8.546193779070014}, 'chosen_samples': [9126, 13000, 55891, 36674, 29941], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.47558097599995, 'batch_acquisition_elapsed_time': 0.0025783210003282875})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.38882978723404255, 'nll': 7.758244902149479}, 'chosen_samples': [46794, 79247, 36865, 85770, 6811], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.38621769600013, 'batch_acquisition_elapsed_time': 0.0025801389997468505})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4068617021276596, 'nll': 7.422868487924338}, 'chosen_samples': [70095, 43327, 79871, 61410, 44060], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.98580072999994, 'batch_acquisition_elapsed_time': 0.0027878129999407975})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4135106382978723, 'nll': 7.22074918112159}, 'chosen_samples': [89800, 6174, 18738, 105014, 543], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.65845908900019, 'batch_acquisition_elapsed_time': 0.002703419999761536})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.40845744680851065, 'nll': 8.251426709934751}, 'chosen_samples': [65849, 77908, 50197, 106410, 30192], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.65387689199997, 'batch_acquisition_elapsed_time': 0.002850647000286699})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.42138297872340424, 'nll': 6.864560610234104}, 'chosen_samples': [42748, 111504, 9102, 104439, 86618], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.74724296799968, 'batch_acquisition_elapsed_time': 0.002812946000176453})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4308510638297872, 'nll': 7.057052887059907}, 'chosen_samples': [82718, 95661, 66334, 65984, 13809], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.3275078329998, 'batch_acquisition_elapsed_time': 0.0024660359999870707})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.42829787234042555, 'nll': 6.209797113151311}, 'chosen_samples': [109378, 100485, 76987, 54955, 37354], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 39.31540063700004, 'batch_acquisition_elapsed_time': 0.0027170969997314387})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4032978723404255, 'nll': 7.336217331441793}, 'chosen_samples': [40281, 59870, 32936, 87099, 65919], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.87390619400003, 'batch_acquisition_elapsed_time': 0.0026539880000200355})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.43659574468085105, 'nll': 5.994328694860986}, 'chosen_samples': [69631, 106678, 13339, 84694, 35516], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 38.62068135499976, 'batch_acquisition_elapsed_time': 0.002715829999942798})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4503191489361702, 'nll': 5.38975364715178}, 'chosen_samples': [8880, 42851, 47877, 69794, 85501], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.731064618000346, 'batch_acquisition_elapsed_time': 0.002563457999713137})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.4426063829787234, 'nll': 5.554039069547614}, 'chosen_samples': [107307, 87535, 6629, 107565, 60394], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 40.25789847399983, 'batch_acquisition_elapsed_time': 0.0027537169999050093})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.44372340425531914, 'nll': 5.168472535869225}, 'chosen_samples': [92887, 41950, 111696, 110676, 17021], 'chosen_samples_score': [0.0, 0.0, 0.0, 0.0, 0.0], 'chosen_samples_orignal_score': None, 'train_model_elapsed_time': 41.413864899000146, 'batch_acquisition_elapsed_time': 0.002767904000393173})
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